With rapid development of computer technologies, computers are becoming more powerful, and application fields are becoming wider. Deep learning is a new field of a machine learning process and aims to establish a neural network that simulates a human brain for analysis and learning, and a category to which data belongs is identified by using the neural network.
In deep learning, “deep” is mainly contrasted with shallow neural network learning when a computer data processing capability is relatively low, and indicates that a neural network has many layers, where there are a large number of neurons at each layer; and “learning” means training a machine such as a computer to identify features of sample data, such as image information and text information. Therefore, it can be seen that, in deep learning, data features are extracted from original data by using a neural network, and these currently unexplainable features usually make a classification effect better.
At present, in a process of training a deep neural network, an unsupervised auto-encoding model is used at each layer, and using the unsupervised autoencoder model to train the deep neural network can minimize a reconstruction error. However, because the unsupervised autoencoder model does not have category information of sample data, a finally obtained deep neural network cannot acquire category information corresponding to input sample data.
Therefore, it can be seen that a problem that a deep neural network fails to identify data category information during data identification exists.